U.S. patent application number 12/836441 was filed with the patent office on 2010-11-04 for monitoring of chronobiological rhythms for disease and drug management using one or more implantable device.
Invention is credited to Marina V. Brockway, Gerrard M. Carlson, Yousufali Dalal, Carlos Haro, John D. Hatlestad, Richard O. Kuenzler, Kent Lee, Abhilash Patangay, Krzysztof Z. Siejko, Yi Zhang.
Application Number | 20100280564 12/836441 |
Document ID | / |
Family ID | 39254002 |
Filed Date | 2010-11-04 |
United States Patent
Application |
20100280564 |
Kind Code |
A1 |
Zhang; Yi ; et al. |
November 4, 2010 |
MONITORING OF CHRONOBIOLOGICAL RHYTHMS FOR DISEASE AND DRUG
MANAGEMENT USING ONE OR MORE IMPLANTABLE DEVICE
Abstract
The health state of a subject is automatically evaluated or
predicted using at least one implantable device. In varying
examples, the health state is determined by sensing or receiving
information about at least one physiological process having a
circadian rhythm whose presence, absence, or baseline change is
associated with impending disease, and comparing such rhythm to
baseline circadian rhythm prediction criteria. Other
chronobiological rhythms beside circadian may also be used. The
baseline prediction criteria may be derived using one or more past
physiological process observation of the subject or population of
subjects in a non-disease health state. The prediction processing
may be performed by the at least one implantable device or by an
external device in communication with the implantable device.
Systems and methods for invoking a therapy in response to the
health state, such as to prevent or minimize the consequences of
predicted impending heart failure, are also discussed.
Inventors: |
Zhang; Yi; (Plymouth,
MN) ; Hatlestad; John D.; (Maplewood, MN) ;
Carlson; Gerrard M.; (Champlin, MN) ; Dalal;
Yousufali; (Irvine, CA) ; Brockway; Marina V.;
(Shoreview, MN) ; Lee; Kent; (Shoreview, MN)
; Kuenzler; Richard O.; (Shaker Heights, OH) ;
Haro; Carlos; (St. Paul, MN) ; Siejko; Krzysztof
Z.; (Maple Grove, MN) ; Patangay; Abhilash;
(Inver Grove Heights, MN) |
Correspondence
Address: |
SCHWEGMAN, LUNDBERG & WOESSNER/BSC-CRM
PO BOX 2938
MINNEAPOLIS
MN
55402
US
|
Family ID: |
39254002 |
Appl. No.: |
12/836441 |
Filed: |
July 14, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11554986 |
Oct 31, 2006 |
7764996 |
|
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12836441 |
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Current U.S.
Class: |
607/3 ;
600/301 |
Current CPC
Class: |
A61B 5/053 20130101;
A61B 5/024 20130101; A61N 1/36114 20130101; A61N 1/365 20130101;
A61B 5/0215 20130101; A61N 1/3614 20170801; G16H 20/40 20180101;
A61B 5/0816 20130101; A61B 5/02405 20130101; A61N 1/36585 20130101;
A61M 5/1723 20130101; G16H 20/10 20180101; G16H 50/30 20180101;
A61B 5/1116 20130101; G16H 50/20 20180101; A61B 5/02055 20130101;
A61B 5/686 20130101; A61N 1/3621 20130101; A61B 5/7275
20130101 |
Class at
Publication: |
607/3 ;
600/301 |
International
Class: |
A61N 1/02 20060101
A61N001/02; A61B 5/00 20060101 A61B005/00 |
Claims
1. A method comprising: sensing or receiving at an implantable
device, information about at least one physiological process having
a chronobiological rhythm whose presence, absence, or change is
statistically associated with a disease; comparing the
chronobiological rhythm of the at least one physiological process
to one or more chronobiological rhythm prediction criteria; and at
least one of predicting, detecting, or identifying an occurrence of
disease using the comparison.
2. The method of claim 1, wherein predicting the occurrence of
disease includes predicting an occurrence of impending disease
occurring during a specified prediction time period.
3. The method of claim 1, wherein sensing or receiving the
information about the at least one physiological process includes
sensing or receiving at least one of body temperature, heart rate,
heart rate variability, respiration rate, respiration rate
variability, minute ventilation, tidal volume, activity, blood
pressure, posture, sleep pattern, thoracic impedance, or at least
one heart sound.
4. The method of claim 1, further comprising providing an
associated collection time to the chronobiological rhythm of the at
least one physiological process.
5. The method of claim 1, further comprising sensing or receiving
information about at least one arrhythmia incidence; and wherein
predicting the occurrence of disease includes using a time of day
of the arrhythmia incidence.
6. The method of claim 1, wherein predicting the occurrence of
disease is computed using one or more stored weighting factor, each
weighting factor corresponding to a chronobiological rhythm of a
different one of the at least one physiological process.
7. The method of claim 1, further comprising adjusting or
initiating a therapy using the predicted, detected, or identified
occurrence of disease.
8. The method of claim 7, wherein adjusting or initiating the
therapy includes determining a drug delivery time using the
chronobiological rhythm of the at least one physiological
process.
9. The method of claim 7, wherein adjusting or initiating the
therapy includes recovering the chronobiological rhythm of the at
least one physiological process using one or both of drug delivery
or neurostimulation.
10. The method of claim 7, further comprising monitoring the
efficacy of the therapy using a post-therapy chronobiological
rhythm of the at least one physiological process.
11. A method comprising: sensing or receiving at an implantable
device, information about at least one physiological process having
a chronobiological rhythm whose presence, absence, or change is
statistically associated with a disease; comparing the
chronobiological rhythm of the at least one physiological process
to one or more chronobiological rhythm prediction criteria; and
applying a therapy.
12. The method of claim 11, wherein applying the therapy includes
using the comparison of the chronobiological rhythm and the one or
more chronobiological rhythm prediction criteria.
13. The method of claim 11, wherein applying the therapy includes
using a subject-responsive drug delivery time derived using one or
more past post-therapy chronobiological rhythm observations from a
subject in a similar pre-therapy disease-state.
14. The method of claim 11, further comprising monitoring the
efficacy of the therapy using a post-therapy chronobiological
rhythm of the at least one physiological process.
15. The method of claim 11, wherein sensing or receiving the
information about the at least one physiological process includes
sensing or receiving at least one of body temperature, heart rate,
heart rate variability, respiration rate, respiration rate
variability, minute ventilation, tidal volume, activity, blood
pressure, posture, sleep pattern, thoracic impedance, or at least
one heart sound.
16. The method of claim 11, further comprising providing an
associated collection time to the chronobiological rhythm of the at
least one physiological process.
17. The method of claim 11, further comprising predicting the
occurrence of the disease by computing probability using one or
more stored weighting factor, each weighting factor corresponding
to a chronobiological rhythm of a different one of the at least one
physiological process.
18. A method comprising: applying a therapy to a subject; and
monitoring the efficacy of the therapy, including sensing or
receiving at an implantable device a post-therapy chronobiological
rhythm associated with at least one of body temperature, heart
rate, heart rate variability, respiration rate, respiration rate
variability, minute ventilation, tidal volume, activity, blood
pressure, posture, sleep pattern, thoracic impedance, or at least
one heart sound.
19. The method of claim 18, wherein applying the therapy includes
delivering one or both of drug or electrical stimulation therapy to
the subject.
20. The method of claim 18, further comprising titrating the
therapy using the monitored efficacy of the therapy.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application is a divisional of U.S. application Ser.
No. 11/554,986, filed Oct. 31, 2006, which is hereby incorporated
by reference in its entirety.
TECHNICAL FIELD
[0002] This patent document pertains generally to medical systems
and methods. More particularly, but not by way of limitation, this
patent document pertains to monitoring of chronobiological rhythms,
such as circadian rhythms, for disease and drug management using
one or more implantable device.
BACKGROUND
[0003] Heart failure ("HF") is a condition in which a subject's
heart can't pump the needed amount of blood to the subject's other
organs causing fluid to build up behind the heart. HF is one of the
leading causes of death in the United States and a leading cause of
poor quality of life in the human population over the age of 65.
There are currently about 5 million or more cases of HF in the
United States alone, with about 1 million of them hospitalized each
year. As the population of subjects 65 years of age and older grows
(i.e., amid the aging of the baby boomer generation), HF threatens
a dramatic increase of morbidity and mortality, along with being a
burgeoning drain on healthcare funds in the United States and other
countries.
[0004] Some of many needs for HF subjects is accurately predicting,
monitoring, and treating heart failure decompensation before an
advanced disease stage is reached. Heart failure, and more
particularly heart failure decompensation, may signify the drawing
near of death or, at the very least, the need for extensive
hospitalization intervention. With sufficient warning, steps
including drug or electrical stimulus therapy can be initiated or
adjusted to save the HF subjects from either of these advanced HF
consequences. Unfortunately, the time associated with typical HF
detection is often too late in the disease process to prevent
significant clinical intervention (e.g., hospitalization) or
death.
Overview
[0005] The health state of a subject is automatically evaluated or
predicted using at least one implantable device. In varying
examples, the health state is determined by sensing or receiving
information about at least one physiological process having a
circadian rhythm whose presence, absence, or baseline change is
associated with impending disease, and comparing such rhythm to
baseline circadian rhythm prediction criteria. Other
chronobiological rhythms beside circadian may also be used. The
baseline prediction criteria may be derived using one or more past
physiological process observation of the subject or population of
subjects in a non-disease health state. The prediction processing
may be performed by the at least one implantable device or by an
external device in communication with the implantable device.
Systems and methods for invoking a therapy in response to the
health state, such as to prevent or minimize the consequences of
predicted impending heart failure, are also discussed.
[0006] In Example 1, a system comprises a prediction criteria
module, adapted to store information about one or more
chronobiological rhythm prediction criteria; a physiological
information collection device, adapted to sense or receive
information about at least one physiological process having a
chronobiological rhythm whose presence, absence, or change is
statistically associated with a disease state; an impending disease
state prediction module, coupled to the prediction criteria module
to receive the one or more chronobiological rhythm prediction
criteria and coupled to the physiological information collection
device to receive the chronobiological rhythm of the at least one
physiological process, the impending disease state prediction
module being adapted to predict an occurrence of impending disease
using the one or more chronobiological rhythm prediction criteria
and the chronobiological rhythm of the at least one physiological
process; and at least one of the prediction criteria module, the
physiological information collection device, or the impending
disease state prediction module including an implantable
portion.
[0007] In Example 2, the system of Example 1 is optionally
configured such that the impending disease state prediction module
is adapted to predict the occurrence of impending disease during a
specified prediction time period.
[0008] In Example 3, the system of Examples 1-2 is optionally
configured such that the information about the at least one
physiological process is sensed or received, at least in part,
using an implantable device or sensor.
[0009] In Example 4, the system of Examples 1-3 is optionally
configured such that the at least one physiological process
includes one or more of body temperature, heart rate, heart rate
variability, respiration rate, respiration rate variability, minute
ventilation, tidal volume, activity, blood pressure, posture, sleep
pattern, thoracic impedance, or at least one heart sound.
[0010] In Example 5, the system of Example 4 optionally includes a
timing circuit coupled to the physiological information collection
device to provide an associated collection time to the
chronobiological rhythm of the at least one physiological process;
and wherein the associated collection time is used by the impending
disease state prediction module to predict the occurrence of
impending disease.
[0011] In Example 6, the system of Examples 1-5 optionally includes
an arrhythmia detector adapted to sense or receive information
about an arrhythmia incidence; and wherein a time of the arrhythmia
incidence is used by the impending disease state prediction module
to predict the occurrence of impending disease.
[0012] In Example 7, the system of Examples 1-6 is optionally
configured such that the predicted occurrence of impending disease
is computed using one or more stored weighting factor, each
weighting factor corresponding to a chronobiological rhythm of a
different one of the at least one physiological process.
[0013] In Example 8, the system of Examples 1-7 is optionally
configured such that the chronobiological rhythm prediction
criteria are derived using one or more past physiological process
observation from a subject in a non-disease state.
[0014] In Example 9, the system of Examples 1-8 optionally includes
a therapy control module adapted to adjust or initiate a therapy
using the predicted occurrence of impending disease.
[0015] In Example 10, the system of Example 9 optionally includes
an implantable drug pump, coupled to the therapy control module to
receive one or more drug delivery instruction.
[0016] In Example 11, the system of Example 9 optionally includes a
neural stimulation circuit, coupled to the therapy control module
to receive one or more neurostimulation delivery instruction.
[0017] In Example 12, the system of Example 9 optionally includes
at least one of a ventricular or atrial stimulation circuit,
coupled to the therapy control module to receive one or more
cardiac stimulation delivery instruction.
[0018] In Example 13, a method comprises sensing or receiving at an
implantable device, information about at least one physiological
process having a chronobiological rhythm whose presence, absence,
or change is statistically associated with a disease; comparing the
chronobiological rhythm of the at least one physiological process
to one or more chronobiological rhythm prediction criteria; and at
least one of predicting, detecting, or identifying an occurrence of
disease using the comparison.
[0019] In Example 14, the method of Example 13 is optionally
configured such that predicting the occurrence of disease includes
predicting an occurrence of impending disease occurring during a
specified prediction time period.
[0020] In Example 15, the method of Examples 13-14 is optionally
configured such that sensing or receiving the information about the
at least one physiological process includes sensing or receiving at
least one of body temperature, heart rate, heart rate variability,
respiration rate, respiration rate variability, minute ventilation,
tidal volume, activity, blood pressure, posture, sleep pattern,
thoracic impedance, or at least one heart sound.
[0021] In Example 16, the method of Examples 13-15 optionally
includes sensing or receiving information about at least one
arrhythmia incidence; and wherein predicting the occurrence of
disease includes using a time of day of the arrhythmia
incidence.
[0022] In Example 17, the method of Examples 13-16 optionally
includes adjusting or initiating a therapy using the predicted,
detected, or identified occurrence of disease.
[0023] In Example 18, the method of Example 17 is optionally
configured such that adjusting or initiating the therapy includes
determining a drug delivery time using the chronobiological rhythm
of the at least one physiological process.
[0024] In Example 19, the method of Example 17 is optionally
configured such that adjusting or initiating the therapy includes
recovering the chronobiological rhythm of the at least one
physiological process using one or both of drug delivery or
neurostimulation.
[0025] In Example 20, the method of Example 17 optionally includes
monitoring the efficacy of the therapy using a post-therapy
chronobiological rhythm of the at least one physiological
process.
[0026] In Example 21, a method comprises sensing or receiving at an
implantable device, information about at least one physiological
process having a chronobiological rhythm whose presence, absence,
or change is statistically associated with a disease; comparing the
chronobiological rhythm of the at least one physiological process
to one or more chronobiological rhythm prediction criteria; and
applying a therapy.
[0027] In Example 22, the method of Example 21 is optionally
configured such that applying the therapy includes using the
comparison of the chronobiological rhythm and the one or more
chronobiological rhythm prediction criteria.
[0028] In Example 23, the method of Examples 21-22 is optionally
configured such that applying the therapy includes using a
subject-responsive drug delivery time derived using one or more
past post-therapy chronobiological rhythm observations from a
subject in a similar pre-therapy disease-state.
[0029] In Example 24, the method of Examples 21-23 optionally
includes monitoring the efficacy of the therapy using a
post-therapy chronobiological rhythm of the at least one
physiological process.
[0030] In Example 25, a method comprises applying a therapy to a
subject; and monitoring the efficacy of the therapy, including
sensing or receiving at an implantable device a post-therapy
chronobiological rhythm associated with at least one of body
temperature, heart rate, heart rate variability, respiration rate,
respiration rate variability, minute ventilation, tidal volume,
activity, blood pressure, posture, sleep pattern, thoracic
impedance, or at least one heart sound.
[0031] In Example 26, the method of Example 25 is optionally
configured such that applying the therapy includes delivering one
or both of drug or electrical stimulation therapy to the
subject.
[0032] In Example 27, the method of Examples 25-26 optionally
includes titrating the therapy using the monitored efficacy of the
therapy.
BRIEF DESCRIPTION OF THE DRAWINGS
[0033] In the drawings, which are not necessarily drawn to scale,
like numerals describe substantially similar components throughout
the several views. The drawings illustrate generally, by way of
example, but not by way of limitation, various embodiments
discussed in the present document.
[0034] FIG. 1 is a schematic view illustrating a system adapted to
predict, monitor, or treat an occurrence of impending heart failure
or other disease state in a subject.
[0035] FIG. 2 is a block diagram illustrating one conceptual
example of a system adapted to predict, monitor, or treat an
occurrence of impending heart failure or other disease state in a
subject.
[0036] FIG. 3 is a block diagram illustrating one conceptual
example of a rhythm collection module.
[0037] FIG. 4 is a block diagram illustrating one conceptual
example of an impending disease state prediction module.
[0038] FIG. 5 is a block diagram illustrating one conceptual
example of a therapy control module.
[0039] FIG. 6 is a block diagram illustrating exemplary
physiological processes having circadian rhythms that may be used
to predict, monitor, or treat an occurrence of impending heart
failure or other disease state in a subject.
[0040] FIGS. 7A-7C are graphical illustrations that may be used by
a subject or caregiver to predict, monitor, or treat an occurrence
of impending heart failure or other disease state in the
subject.
[0041] FIG. 8 illustrates a method of predicting, monitoring, or
treating an occurrence of impending heart failure or other disease
state in a subject.
DETAILED DESCRIPTION
[0042] The following detailed description includes references to
the accompanying drawings, which form a part of the detailed
description. The drawings show, by way of illustration, specific
embodiments in which the present systems and methods may be
practiced. These embodiments, which are also referred to herein as
"examples," are described in enough detail to enable those skilled
in the art to practice the present systems and methods. The
embodiments may be combined, other embodiments may be utilized or
structural, electrical, or logical changes may be made without
departing from the scope of the present systems and methods. The
following detailed description is, therefore, not to be taken in a
limiting sense, and the scope of the present systems and methods
are defined by the appended claims and their legal equivalents.
[0043] In this document, the terms "a" or "an" are used to include
one or more than one; the term "or" is used to refer to a
nonexclusive "or" unless otherwise indicated; the term "subject" is
used to include the term "patient"; and the terms "predict,"
"prediction," or other variants thereof are used to denote a
probability assertion or statement regarding whether or not an
occurrence of impending heart failure or other disease state might
occur during a specified time period. In addition, it is to be
understood that the phraseology or terminology employed herein, and
not otherwise defined, is for the purpose of description only and
not of limitation.
[0044] Furthermore, all patents and patent documents referred to in
this document are incorporated by reference herein in their
entirety, as though individually incorporated by reference. In the
event of inconsistent usages between this document and those
documents so incorporated by reference, the usage in the
incorporated references should be considered supplementary to that
of this document; for irreconcilable inconsistencies, the usage in
this document controls.
[0045] Introduction
[0046] HF and other disease states are associated with a loss or
baseline change of one or more circadian rhythms, especially when
the subject decompensates. A subject's body, when relatively
healthy (i.e., in a non-disease state), has more than 100 circadian
rhythms. Each circadian rhythm is a unique, roughly 24-hour cycle
of a subject's physiological process, such as body temperature
(core or peripheral), heart rate, heart rate variability,
respiration rate, respiration rate variability, minute ventilation,
activity, blood pressure, posture, tidal volume, sleep quality or
duration, thoracic impedance, or heart sounds, among others.
[0047] The present systems and methods may predict, monitor, or
treat an impending disease state of a subject, such as the
likelihood of an occurrence of heart failure, using circadian or
other rhythm monitoring. In certain examples, treating the
impending disease state of the subject includes adjusting or
initiating one or more therapies (e.g., drug therapy or
neurostimulation), such as to prevent, decrease, or minimize such
predicted impending disease state or monitor the efficacy of such
applied therapy. In certain examples, monitoring the impending
disease state of the subject includes monitoring the subject's
recovery from the impending disease state in light of the applied
therapy.
[0048] As will be discussed below, the prediction, monitoring, or
treatment of an impending disease state can be made by sensing or
receiving one or more circadian (or other chronobiological) rhythms
associated with a subject's physiological process and by comparing
such rhythm(s) to one or more baseline chronobiological rhythm
prediction criteria that are derived by a caregiver (e.g., a
physician) or from at least one subject in a non-disease state.
Advantageously, prediction, monitoring, or treatment of an
impending disease state, such as heart failure, may reduce or
eliminate the need for hospital intervention, and may be useful for
avoiding a decompensation crisis and properly managing a heart
failure subject in a state of relative well-being.
EXAMPLES
[0049] The techniques of the present systems and methods may be
used in applications involving implantable medical devices ("IMDs")
including, but not limited to, implantable cardiac rhythm
management ("CRM") systems such as pacemakers,
cardioverters/defibrillators, pacemakers/defibrillators,
biventricular or other multi-site resynchronization or coordination
devices such as cardiac resynchronization therapy ("CRT") devices,
patient monitoring systems, neural modulation systems, and drug
delivery systems. In addition, the systems and methods described
herein may also be employed in unimplanted devices, including but
not limited to, external pacemakers, neutral stimulators,
cardioverters/defibrillators, pacer/defibrillators, biventricular
or other multi-site resynchronization or coordination devices,
monitors, programmers and recorders, whether such devices are used
for providing sensing, receiving, prediction processing, or
therapy.
[0050] FIG. 1 is a schematic view illustrating one example of a
system 100 adapted to predict, monitor, or treat an occurrence of
impending heart failure or other disease state in a subject 110
using sensed or received information about at least one
physiological process having a circadian rhythm whose presence,
absence, or baseline change is statistically associated with a
disease state, and an environment in which the system 100 may be
used. As shown in FIG. 1, the system 100 may include an IMD 102,
such as a CRM device, which can be coupled by at least one lead 108
to a heart 106 or efferent parasympathetic nerve, such as a vagus
nerve 107, of the subject 110. The IMD 102 may be implanted
subcutaneously in the subject's chest, abdomen, or elsewhere. Each
of the at least one lead 108 extends from a lead proximal portion
114 to a lead distal portion 112.
[0051] The exemplary system 100 also includes a physiological
information collection device 104, remote portions (e.g., a nearby
external user-interface 120 or a distant external user interface
122) of which are shown in FIG. 1, a drug delivery system (e.g., a
drug pump 116), and a warning device 118. The remote portions 120,
122 of the physiological information collection device 104 may
provide wireless communication with the IMD 102 and with one
another using telemetry 150 or other known communication
techniques. In one example, the prediction, monitoring, or
treatment of the occurrence of impending heart failure or other
disease state is made, at least in part, by receiving information
about at least one physiological process having a circadian rhythm
remotely (e.g., at the nearby 120 or distant 122 external user
interface) and then communicating signals representative of the
circadian rhythm, or lack thereof, to the IMD 102 for processing.
In certain examples, the remote portions of the physiological
information collection device 104 include a visual or other display
124, such as a LCD or LED display, for textually or graphically
relaying information to the subject 110 or a caregiver regarding
operation, findings (e.g., loss or baseline change of one or more
circadian rhythms; recovery of the one or more circadian rhythms),
or predictions of the system 100.
[0052] The drug pump 116 or another drug dispensing device may be
provided in addition to the IMD 102 to control the delivering of
one or more therapy drug to the subject 110 or, if already doing
so, to adjust or terminate a dosage of the delivered drug(s). The
efficacy of the drug therapy may be evaluated based on changes, if
any, in the circadian rhythms of the at least one physiological
process sensed or received by the physiological information
collection device 104. For instance, if the system 100 initially
detects a loss or baseline change of one or more of a subject's
circadian rhythms (e.g., relative to one or more baseline circadian
rhythm prediction criteria) and thereafter directs the drug pump
116 to deliver a diuretic or other drug in an attempt to regain
normal (or non-disease like) circadian rhythm(s), the efficacy of
such diuretic drug therapy and the subject's 110 recovery state may
be evaluated by monitoring post-therapy circadian rhythm(s) of at
least one physiological process. In a similar manner, the efficacy
of electrical stimulation therapy, such as neurostimulation
therapy, may be evaluated.
[0053] If the system 100, based on circadian rhythm monitoring,
comes to the conclusion that an occurrence of heart failure (for
example) is likely to occur during a predicted future time period
for the subject 110, one or more warning signals may be made to the
subject or his/her caregiver. Warning signals may be generated
using either an internal warning device 118 or the external user
interfaces 120, 122 so-as-to notify the subject 110 or his/her
caregiver of the onset of heart failure or other disease state. The
internal warning device 118 may be a vibrating or audible device
that provides perceptible stimulation to the subject 110 to alert
him/her of any significant progression of heart failure so that
he/she may immediately consult their caregiver. The external user
interfaces 120, 122 may provide audible alarm signals to alert the
subject 110 as well textual or graphic displays. In addition, once
impending heart failure has been predicted by the system 100,
information used to make the prediction (e.g., loss of one or more
circadian rhythms) is stored within the IMD 102 or sent to the
distant external user interface 122 for review by the caregiver. If
warranted, the caregiver may then initiate or modify a (stimulation
or drug) therapy or adjust control parameters of the IMD 102 or
drug pump 116.
[0054] FIG. 2 provides a simplified block diagram illustrating one
conceptual example of a system 100 adapted to predict, monitor, or
treat an occurrence of impending heart failure or other disease
state in a subject 110 (FIG. 1). In certain examples, treating the
impending HF or other disease state includes adjusting or
initiating one or more therapies, such as electrical stimulation or
drug therapy. In certain examples, monitoring the impending HF or
other disease state includes monitoring the subject's 110 recovery
from the impending disease in light of the applied therapy.
[0055] FIG. 2 further illustrates an exemplary placement of a
plurality of leads 108A, 108B, 108C, specifically lead distal end
portions, within, on, or near a heart 106 of the subject 110. As
shown, the heart 106 includes (among other things) a right atrium
200A, a left atrium 200B, a right ventricle 202A, and a left
ventricle 202B. In this example, an atrial lead 108A includes
electrodes disposed in, around, or near the right atrium 200A of
the heart 106, such as a ring electrode 204 and a tip electrode
206, for sensing signals (e.g., via atrial sensing circuit 250) or
delivering pacing or other stimulation therapy (e.g., via atrial
stimulation circuit 252) to the right atrium 200A. The atrial lead
108A may also include additional electrodes, such as for delivering
atrial or ventricular cardioversion/defibrillation or pacing
therapy to the heart 106.
[0056] In FIG. 2, a right ventricular lead 108B is also shown and
includes one or more electrodes, such as a tip electrode 208 and a
ring electrode 210, for sensing signals (e.g., via ventricular
sensing circuit 254) or delivering pacing or other stimulation
therapy (e.g., via ventricular stimulation circuit 256). The right
ventricular lead 108B may also include additional electrodes, such
as one or more coil electrode 212A or 212B for delivering atrial or
ventricular cardioversion/defibrillation or pacing therapy to the
heart 106. Further, the system 100 of FIG. 2 also includes a left
ventricular lead 108C, which provides one or more electrodes such
as a tip electrode 214 and a ring electrode 216, for sensing
signals or delivering pacing or other stimulation therapy. The left
ventricular lead 108C may also include one or more additional
electrodes, such as coil electrodes 218A or 218B for delivering
atrial or ventricular cardioversion/defibrillation or pacing
therapy to the heart 106.
[0057] As shown, the IMD 102 includes electronic circuitry
components that are enclosed in a hermetically-sealed enclosure,
such as a can 220. Additional electrodes may be located on or near
an efferent parasympathetic or afferent nerve, on the can 220, on
an insulating header 222, or on other portions of the IMD 102, such
as for sensing or for providing neurostimulation, pacing, or
defibrillation energy, for example, with or without the electrodes
disposed within, on, or near the heart 106. Other forms of
electrodes include meshes and patches that may be applied to
portions of the heart 106 or that may be implanted in other areas
of the body to help direct electrical currents produced by the IMD
102. For example, a warning electrode 118 on the insulating header
222 may be used to stimulate local muscle tissue to provide an
alert/warning of a prediction of impending disease to the subject
110. The present systems and methods are adapted to work in a
variety of electrode configurations and with a variety of
electrical contacts or electrodes in addition to the electrode
configuration shown in FIG. 2.
[0058] It is to be noted that FIG. 2 illustrates just one
conceptualization of various modules, circuits, and interfaces of
system 100, which are implemented either in hardware or as one or
more sequences of steps carried out on a microprocessor or other
controller. Such modules, devices, and interfaces are illustrated
separately for conceptual clarity; however, it is to be understood
that the various modules, devices, and interfaces of FIG. 2 need
not be separately embodied, but may be combined or otherwise
implemented. The IMD 102, in particular, may be powered by a power
source 230, such as a battery, which provides operating power to
all the IMD internal modules and circuits shown in FIG. 2. In
certain examples, the power source 230 should be capable of
operating at low current drains for long periods of time and also
be capable of providing high-current pulses (for capacitor
charging) when the subject 110 (FIG. 1) requires a shock pulse.
[0059] In this example, the system 100 further includes a
physiological information collection device 104 adapted to sense or
receive information about at least one physiological process having
a circadian rhythm whose presence, absence, or baseline change is
statistically associated with a disease state. In varying examples,
the at least one physiological process includes one or more of body
temperature (core or peripheral), heart rate, heart rate
variability, respiration rate, respiration rate variability, minute
ventilation, activity, blood pressure, posture, tidal volume, sleep
quality or duration, thoracic impedance, or heart sounds. Circadian
rhythm representative signals associated with the at least one
physiological process may be output to a programmable controller
224 for performing the prediction, monitoring, or treatment of the
occurrence of impending heart failure or other disease state.
Additionally or alternatively, a time of the circadian rhythm
representative signal collection, a clinical event, or an
arrhythmia incidence (atrial or ventricular) may be output to the
programmable controller 224 and used in the prediction, monitoring,
or treatment. For instance, it has been found that certain
diseases, such as pulmonary edema, tend to disrupt (i.e., lose or
change from baseline) at least one physiological process's
circadian rhythm at certain times of a day or week. Using such
information, one (e.g., a caregiver or the IMD 102 itself) can more
easily treat the impending disease.
[0060] As shown, the physiological information collection device
104 may include an atrial sensing circuit 250, a ventricular
sensing circuit 254, a first information sensor module 226, a
second information sensor module 228, a communication module 232, a
(nearby) external user interface 120 (e.g., a home station device),
an external communication repeater 236, an Internet or other
communication network connection 238, a computerized medical data
storage 240, or a (distant) external user interface 122 (e.g., a
physician station device).
[0061] The atrial 250 and ventricular 254 sensing circuits, the
first information sensor module 226, and the communication module
232 may be directly coupled to the programmable controller 224;
while the second information sensor module 228, the (nearby)
external user interface 120, and the external communication
repeater 236 may be communicatively coupled with the communication
module 232 via telemetry, and thus also be in communication with
the programmable controller 224. In this example, the communication
module 232 is capable of wirelessly communicating with the
computerized medical data storage 240 or the (distant) external
user interface 122, such as by using the external communication
repeater 236 and the Internet/phone connection 238. In one example,
the nearby 120 or distant 122 external user interface controls,
loads, or retrieves information from the IMD 102, and is adapted to
process and display (e.g., textually or graphically) such
information obtained.
[0062] The atrial 250 and ventricular 254 sensing circuits may be
selectively coupled to the atrial lead 108A, the right ventricular
lead 108B, or the left ventricular lead 108C, via an electrode
configuration switching circuit 244, for detecting the presence of
intrinsic cardiac activity in each of the four chambers of the
heart 106. These intrinsic heart activity signals typically include
depolarizations that propagate through the circulatory system. The
depolarizations cause heart contractions for pumping blood through
the circulatory system. The atrial 250 and ventricular 254 sensing
circuits may include dedicated sense amplifiers, multiplexed
amplifiers, shared amplifiers, or other signal processing circuits
to extract depolarizations or other useful information from the
intrinsic heart activity signals. For instance, each of the atrial
250 or ventricular 254 sensing circuits may employ one or more low
power, precision amplifier with programmable or automatic gain,
bandpass filtering, or a threshold detection circuit, to
selectively sense the cardiac signal of interest.
[0063] For arrhythmia detection 246, the IMD 102 utilizes the
atrial 250 and ventricular 254 sensing circuits to sense cardiac
signals for determining whether a rhythm is normal or
pathologic.
[0064] For thoracic impedance detection, the IMD 102 may inject an
electrical stimulus current of known or attainable value (e.g., via
the ventricular 256 or atrial 252 stimulation circuits) to one or
more implanted electrodes and measure (e.g., via the ventricular
254 or atrial 250 sense circuits) the resulting voltage using one
or more other implanted electrodes. Using information about the
current and the resulting voltage, the IMD 102 may calculate an
impedance by taking a ratio of resulting voltage to injected
current. This measurement may be repeated over time to detect
changes in impedance (and thus changes in fluid amount in the
lungs). A reduction in thoracic impedance indicates the presence of
an increase in fluid within the lungs. Conversely, a fluid decrease
in the lungs corresponds to an increase in thoracic impedance
sensed.
[0065] In FIG. 2, the first 226 and second 228 information sensor
modules include one or more physiologic process sensors, such as a
temperature sensor 260, a blood pressure sensor 258, a respiratory
rate/respiratory rate variability sensor 262, a tidal volume/MV
sensor 264, an activity sensor 270, a heart rate/heart rate
variability sensor 266, a posture sensor 268, or an accelerometer
or microphone 267. In one example, each information sensor module
226, 228 also includes one or more interface circuits that receive
one or more control signals and preprocesses the sensor signal(s)
received. In another example, the first 226 and second 228
information sensor modules are combined as a single module.
[0066] A sleep detector 272 shown associated with the programmable
controller 224 inputs signals from the various physiological
information sensors 258-270 or the nearby external user interface
120 to determine whether the subject 110 is in a state of sleep,
and if so, determines the quality of such sleep. In some examples,
the programmable controller 224 determines whether the subject 110
is attempting to fall asleep based on whether the subject is or is
not in a recumbent position, determinable via the posture sensor
268. In some examples, a sleep state detection system, such as
described in Dalal et al., U.S. patent application Ser. No.
11/458,602 entitled, "SLEEP STATE DETECTION," which is assigned to
Cardiac Pacemakers, Inc., is used to determine whether or not the
subject 110 is in a state of sleep.
[0067] Other ways in which the programmable controller 224 may
identify when the subject 110 (FIG. 1) is attempting to sleep are
as follows. In one example, the programmable controller 224 may
identify the time that the subject 110 begins attempting to fall
asleep based on an indication received from the subject, such as
via nearby external user interface 120 and the communication module
232. In another example, the programmable controller 224 identifies
the time the subject 110 begins attempting to fall asleep based on
the activity level of the subject determined via the activity
sensor 270. The activity sensor 270 may include one or more
accelerometers, gyros, or bonded piezoelectric crystals that
generate a signal as a function of subject activity pattern, such
as body motion, foot strikes or other impact events, and the like.
Additionally or alternatively, the activity sensor 270 may include
one or more electrodes that generate an electromyogram ("EMG")
signal as a function of muscle electrical activity, which may
indicate the activity level of the subject 110. The electrodes may,
for example, be located in the legs, abdomen, cheek, back, or
buttocks of the subject 110 to detect muscle activity associated
with walking, running, or the like.
[0068] The programmable controller 224 includes various functional
modules, circuits, and detectors, one conceptualization of which is
illustrated in FIG. 2. Among other things, the programmable
controller 224 may include control circuitry, a RAM or ROM memory
274, logic and timing circuitry 277 to keep track of the timing of
sensing or receiving circadian rhythm representative signals
associated with physiological processes of the subject 110 (FIG.
1), for example, and I/O circuitry. Additionally, the programmable
controller 224 may include a rhythm collection module 276 that
receives from the physiological information collection device 104
information about the at least one physiological process having a
circadian rhythm whose presence, absence, or baseline change is
associated with a disease state. The rhythm collection module 276
may include the memory 274 to store signals representative of such
circadian rhythm(s) and may further classify such rhythm(s) as
being associated with one or more of body temperature (core or
peripheral), heart rate, heart rate variability, respiration rate,
respiration rate variability, minute ventilation, activity, blood
pressure, posture, tidal volume, sleep quality or duration,
thoracic impedance, or heart sounds.
[0069] In this example, the programmable controller 224 also
includes a prediction criteria module 278 adapted to store one or
more baseline circadian rhythm prediction criteria. In one example,
the one or more baseline circadian rhythm prediction criteria are
derived using one or more past physiological process observation of
the subject when in a non-disease health state (i.e., in a
relatively healthy state). In another example, the one or more
baseline circadian rhythm prediction criteria are derived using one
or more past physiological process observation of a population when
in a non-disease health state. In a further example, the one or
more baseline circadian rhythm prediction criteria are loaded into
the IMD 102 before, during, or after the IMD 102 is implanted in
the subject 110, such as via an external user-interface 120,
122.
[0070] For predicting, monitoring, or treating the occurrence of
impending heart failure or other disease state, the programmable
controller 224 includes an impending disease state prediction
module 280 and a therapy control module 282. The impending disease
state prediction module 280 is coupled to both the prediction
criteria module 278 to receive the one or more baseline circadian
rhythm prediction criteria, and is coupled to the physiological
information collection device 104 (via the rhythm collection module
276) to receive the circadian rhythm representative signals
associated with the at least one physiological process. The
impending disease state prediction module 280 predicts the
likelihood of future heart failure, for example, using the one or
more baseline circadian rhythm prediction criteria and the
circadian rhythm representative signals associated with the at
least one physiological process sensed or received. More
specifically, the impending disease state prediction module 280
predicts the likelihood of impending heart failure based on a
determination of whether or not the circadian rhythm(s) of the at
least one physiological process have been lost or changed (e.g.,
relative to the baseline circadian rhythm prediction criteria).
[0071] The therapy control module 282 is programmed to select (from
a set of available therapies) the most appropriate responsive
therapy (or combination of therapies), such as for reducing the
likelihood or even preventing the predicted occurrence of impending
disease (e.g., heart failure). In one example, the therapy control
module 282 also triggers the delivery of the selected therapy after
determining if the probability of the occurrence of impending
disease state, computed by the impending disease state prediction
module 280, warrants such administration.
[0072] In one example, such therapy is provided via electrodes
associated with the heart 106 or portions of a subject's nervous
system such as, for example, sympathetic or parasympathetic members
of the autonomic nervous system. In one such example, the
electrodes provide neurostimulation via a neural stimulation
circuit 257 in electrical contact with the vagus nerve 107 (FIG. 1)
or a baroreceptor, thereby adjusting autonomic tone to restore tone
indicative of normal circadian rhythm. The vagus nerve 107 provides
parasympathetic stimulation to the heart 106 (FIG. 1) that
counteracts the effects of increased sympathetic activity, and
stimulation of the vagus nerve 107 at either a pre-ganglionic or
post-ganglionic site produces dilation of the coronary arteries and
a reduced workload on the heart 106. Baroreceptors are sensory
nerve endings located in the heart 106 and vasculature that are
stimulated by increased fluid pressure. Stimulation of
baroreceptors causes impulses to be relayed via afferent pathways
to nuclei in the brainstem that result in parasympathetic
activation and sympathetic inhibition.
[0073] A subject's 110 autonomic balance may vary in accordance
with circadian rhythms. To this end, the neural stimulation circuit
257 (via the therapy control module 282) may be programmed to
schedule delivery of neurostimulation in accordance with the
subject's circadian rhythms for increased beneficial effect. The
neural stimulation circuit 257 (via the therapy control module 282)
may be programmed to titrate the delivery of neurostimulation by
scheduling such delivery or adjusting the level of the
neurostimulation in an open- or closed-loop manner that takes into
consideration the effects of the circadian rhythm representative
signals sensed or received.
[0074] In another example, such therapy is provided elsewhere
(e.g., communicated to nearby external user interface 120 or
delivered via a drug pump 116 (FIG. 1)) and includes, for example,
a drug dose, a diet regimen, or a fluid intake regimen. In either
case, the programmable controller 224 may control the therapy
provided in view of any detected recovery or further loss or change
of the subject's circadian rhythms. For instance, the programmable
controller 224 may direct that therapy be increased if the
subject's circadian rhythms are being further lost relative to the
baseline prediction criteria or that the therapy be decreased or
terminated if the subject's circadian rhythms are being recovered
(i.e., regained). Further yet, the programmable controller 224 may
be used to determine the efficacy of any drug or other therapy
administered to the subject 110, such as via drug pump 116.
[0075] Moreover, the programmable controller 224, specifically the
therapy control module 282, can use knowledge of the subject's 110
(FIG. 1) circadian rhythms to determine (1) the time when the
subject needs a therapy the most or (2) the time when the subject
is most responsive to the therapy (i.e., a subject-responsive drug
delivery time), and then deliver the therapy as such. For instance,
in a preclinical study, it was found that thoracic impedance
followed a pattern of low evening/night time impedance (indicative
of more fluid in the subject) followed by an increasing day
time-afternoon impedance (indicative of less fluid in the subject).
Thus, when a specimen was given diuretics during the day, a greater
effect was observed than when diuretics were given during the late
evening. Consequently, such information can be used to direct the
consumption of diuretics or other drugs during the day due to its
greater observed effect. Alternatively or additionally, this
knowledge may be used to determine an expected drug effect give the
time of day it is administered.
[0076] Nearby 120 and distant 122 external user-interfaces may be
used in, among other things, programming the IMD 102. Briefly, the
user-interfaces permit a caregiver or other user to program the
operation of the IMD 102 or to retrieve and display information
(e.g., textually or graphically) received from the IMD 102.
Depending upon the specific programming of the external
user-interfaces 120, 122, each interface may also be capable of
processing and analyzing data received from the IMD 102 and, for
example, render an impending disease state prediction.
[0077] FIG. 3 is a block diagram illustrating one conceptual
example of a portion of a rhythm collection module 276. In one
example, the rhythm collection module 276 includes a classification
module 302 and a detection processing module 304. In such an
example, the rhythm collection module 276 is programmed to
recurrently receive, store, and detect the presence, time (via
timing circuitry 277), and magnitude of the circadian rhythm
representative signals associated with at least one physiological
process sensed or received by the atrial sensing circuit 250, the
ventricular sensing circuit 254, the first information sensor
module 226, or the communication module 232 (communicatively
coupled to the second information module 228, the (nearby) external
user interface 120, and the external communication repeater 236).
The classification module 302 separates the received circadian
rhythm representative signals into one or more associated
physiological process categories, such as body temperature (core or
peripheral), heart rate, heart rate variability, respiration rate,
respiration rate variability, minute ventilation, activity, blood
pressure, posture, tidal volume, sleep quality or duration,
thoracic impedance, or heart sounds. The classified circadian
rhythm representative signals are then output to the detection
processing module 304, which is adapted to detect the presence,
time, or magnitude of the signals received. From the rhythm
collection device 276, the circadian rhythm representative signals
are output to an impending disease state prediction module 280.
[0078] FIG. 4 is a block diagram illustrating one conceptual
example of a portion of an impending disease state prediction
module 280. In one example, the impending disease state prediction
module 280 includes a probability processing module 402 and a
prediction processing module 404. The impending disease state
prediction module 280 includes an input that receives the at least
one circadian rhythm representative signal (S.sub.1, S.sub.2, . . .
, S.sub.N) from the rhythm collection module 276 and includes an
input that receives the baseline circadian rhythm prediction
criteria from the prediction criteria module 278. Optionally, the
impending disease state prediction module 280 includes an input
that receives from an arrhythmia detector 246 or an external
user-interface 120, 122 a time of day of an arrhythmia incident or
a clinical event.
[0079] In one example, the probability processing module 402
includes a weighting module 406 and a probability comparator 408.
After entering the impending disease state prediction module 280,
the at least one circadian rhythm representative signal (S.sub.1,
S.sub.2, . . . , S.sub.N) and the baseline circadian rhythm
prediction criteria are received by the probability processing
module 402. The probability comparator 408 compares one or more
circadian rhythm representative signal (S.sub.1, S.sub.2, . . . ,
S.sub.N) value to one or more corresponding baseline circadian
rhythm prediction criteria (C.sub.1, C.sub.2, . . . , C.sub.N)
value, such as one or more value sensed at a similar time of day
and associated with the same physiological process. In another
example, the at least one circadian rhythm representative signal
(S.sub.1, S.sub.2, . . . , S.sub.N) is analyzed with respect to at
least one other circadian rhythm representative signal associated
with the same physiological process.
[0080] Data analysis and comparison of sensed or received circadian
rhythms may involve both graphical and numerical procedures, and
may further be characterized by one or more of a mean/median level,
an amplitude, a phase, a period, a wave form, or robustness, for
example. For instance, data analysis and comparison techniques that
may be used in the prediction of an occurrence of impending disease
include, among others, spectral analysis such as a strength or
width of the circadian peak of the rhythm spectrum, 24-hour
synchronous averaging, day/night differences, daily minimum/maximum
differences, order statistics such as upper-quartile vs. lower
quartile differences, phase lag/drift/stability with respect to a
24-hour clock, or wake/sleep differences.
[0081] In one example, for each circadian rhythm representative
signal (S.sub.1, S.sub.2, . . . , S.sub.N) value or set of
chronological circadian rhythm values differing by more than a
specified amount from the baseline prediction criteria (C.sub.1,
C.sub.2, . . . , C.sub.N) value or set of values, indicating a loss
of circadian rhythm, the probability comparator 408 summarizes
(e.g., via logistic regression) and outputs to the prediction
processing module 404 a probability indication of the occurrence of
impending disease, such as heart failure. The comparisons may be
discrete or continuous.
[0082] In another example, the weighting module 406 stores
weighting factors (Weight.sub.1, Weight.sub.2, . . . ,
Weight.sub.N), wherein each weighting factor corresponds to a
different one of the circadian rhythm representative signals
received by the probability processing module 402 (i.e., each
weighting factor corresponds to a different physiological process
sensed or received). Weighting factors may be used for computing
the probability indication of the occurrence of an impending
disease state, such as heart failure, by providing a degree to
which each physiological process's circadian rhythm enters into the
probability indication. In one example, each weight (Weight.sub.1,
Weight.sub.2, . . . , Weight.sub.N) is computed using historical
data relating the corresponding circadian rhythm of the
physiological process sensed or received to the occurrence of
impending heart failure, for example. In one such example, the
historical data is obtained from the same subject 110 from whom the
circadian rhythm information of the physiological process is sensed
or received. In another such example, the historical data is
obtained from at least one different subject than the circadian
rhythm information (i.e., the circadian rhythm representative
signal(s)) was obtained from. In a further such example, the
historical data is obtained from a population of subjects.
[0083] Each weight may be computed using not only information about
which physiological process the circadian rhythm is associated
with, but may be computed using information about which other or
how many other physiological process(es)' circadian rhythms also
being used to predict the occurrence of impending heart failure or
other disease state. As an illustrative example, suppose sensed or
received circadian rhythms A and B each have weights of 0.1,
leading to a combined prediction weight of 0.2. In another example,
however, the circadian rhythms A and B each have weights of 0.1
when these rhythms are individually used to predict the occurrence
of impending disease, but have a different (e.g., greater or
lesser) weight when both are present (e.g., stronger weights of 0.5
when both A and B are sufficiently present and used to predict the
occurrence of impending disease). That is, the weight values may
depend on cross-correlation between two or more circadian rhythms.
In a further example, a weight value depends on how many circadian
rhythms are being used to compute the predicted occurrence of
impending disease. As an illustrative example, suppose circadian
rhythm A has a weight of 0.5 when it is used alone for predicting
the occurrence of impending heart failure decompensation. In
another example, however, circadian rhythm A has a weight of 0.25
when used in combination with one other circadian rhythm associated
with a different physiological process (e.g., circadian rhythm B or
circadian rhythm C, etc.).
[0084] In one example, the prediction processing module 404
generates, using the probability indication output from the
probability processing module 402, a probability assertion or
statement that an occurrence of impending disease will occur during
a specified period after the prediction. An example of such a
probability assertion or statement is a 50% probability that an
occurrence of impending heart failure decompensation will occur
during 5 days of the prediction generation. This assertion or
statement of prediction includes both a magnitude (50%) and a well
defined time period during which the prediction is applicable (5
days).
[0085] Impending disease state prediction module 280 outputs an
impending disease state prediction to a therapy control module 282,
which in turn bases control of preventive or other therapy on the
disease state prediction. In one example, as discussed above, the
impending disease state prediction output from the impending
disease state prediction module 280, more particularly the
prediction processing module 404, includes a set of one or more
probability assertions or statements. Each probability statement
includes both a magnitude of the probability (e.g., a 50%
probability of impending heart failure decompensation exists) and a
specified future time period associated therewith (e.g., will occur
within 5 days). In another example, each probability statement also
identifies which circadian rhythm representative signal(s), and
thus which physiological process, contributed to its magnitude.
[0086] In an alternative example, the impending disease state
prediction calculation and output from the impending disease state
prediction module 280 takes the form of a conditional probability
computation, such as described in Sweeney et al., U.S. Pat. No.
6,272,377 entitled, "CARDIAC RHYTHM MANAGEMENT SYSTEM WITH
ARRHYTHMIA PREDICTION AND PREVENTION," Girouard et al., U.S. Pat.
No. 7,127,290 entitled, "CARDIAC RHYTHM MANAGEMENT SYSTEMS AND
METHODS PREDICTING CONGESTIVE HEART FAILURE STATUS," or Brockway et
al., U.S. Pat. No. 7,433,853 entitled, "EXPERT SYSTEM FOR PATIENT
MEDICAL INFORMATION ANALYSIS," each of which are assigned to
Cardiac Pacemakers, Inc. and recite the use of conditional
probabilities to predict the likelihood of occurrence of a future
event. In the present context, the future event is a disease state,
such as heart failure, and the circadian rhythms sensed or received
serve as triggers/markers or, more generally, the conditioning
events. The weights correlating each circadian rhythm
representative signal to a future disease state are conditional
probabilities that may alternatively be expressed as rates, as
described in the above-incorporated Sweeney et al. reference.
[0087] FIG. 5 is a block diagram illustrating one conceptual
example of a therapy control module 282, which may be used to
trigger one or more therapies to a subject 110 (FIG. 1) in response
to a predicted occurrence of an impending disease state. The
therapy control module 282 includes an input that receives the
probability assertions or statements output from the impending
disease state prediction module 280. In one example, a prediction
scheduler 502 schedules the predictions of impending disease, such
as heart failure. A therapy decision module 504 decides whether
therapy is warranted. The therapy selection module 506 selects one
or more appropriate therapies. The control module 508 adjusts the
selected therapy via an output to one or more of an atrial
stimulation circuit 252, a ventricular stimulation circuit 256, a
neural stimulation circuit 257, a nearby external user-interface
120, or a drug pump 116, for example. The therapy control module
282 further includes a therapy list 510, which may include means to
relate the therapies of the therapy list 510 to the circadian
rhythms used by the impending disease state prediction module 280
in predicting the occurrence of impending heart failure, for
example. The various submodules in the therapy control module 282
are illustrated as such for conceptual purposes only; however,
these submodules may alternatively be incorporated in the impending
disease state prediction module 280 or elsewhere. As discussed
below, such as in associated with FIG. 6, a subject's 110 (FIG. 1)
response to the applied therapy may be monitored via the subject's
post-therapy circadian rhythms.
[0088] In one example, the therapy selection module 506 selects a
heart failure preventive therapy using outputs from the therapy
decision module 504. If the therapy decision module 504 determines
that the degree and confidence in the impending disease state
prediction warrants some therapy, then the therapy selection module
506 selects a member of the therapy list 510 to be invoked. In
another example, the therapy section module 506 selects a therapy
that is only secondarily related to the predicted disease
state.
[0089] In another example, the therapy list 510 includes all
possible disease state preventive therapies or secondarily related
therapies that system 100 (FIG. 1) may deliver or communicate to
the subject 110. The therapy list 510 may be programmed into the
IMD 102 either in hardware, firmware, or software. In yet another
example, the therapy list 510 includes immediate, short-term,
intermediate-term, or long-term heart failure preventive therapies.
Immediate heart failure preventive therapies include, by way of
example, initiating or changing a drug therapy administered to a
subject 110 via an implantable drug pump 116 or electrical
stimulation administered to the subject 110 via one or more
electrode bearing leads 108. Short-term heart failure preventive
therapies include, by way of example, administering a continuous
positive air pressure ("CPAP") dose to the subject 110 or notifying
a caregiver to initiate or change the subject's drug treatment
program. Intermediate-term heart failure preventive therapies
include, by way of example, adjusting the subject's 110 (FIG. 1)
lifestyle (e.g., decrease salt or water consumption). Finally,
long-term heart failure preventive therapies include, by way of
example, notifying the subject 110 or caregiver to alter the drug
which takes longer to affect the subject (e.g., beta blockers, ACE
inhibitors) or administering CRT to the subject.
[0090] Each member of the therapy list 510 may be associated with a
required time of action, which includes one or more of a time for
the therapy to become effective or a time after which the therapy
is no longer effective. Accordingly, in one example, the prediction
scheduler 502 considers only those members of the therapy list 510
that can be expected to be effective within a time frame
commensurate with the prediction time period. In another example,
only one member of the therapy list 510 is invoked at any
particular time. In a further example, combinations of different
therapies are provided at substantially the same time.
[0091] FIG. 6 is a block diagram illustrating exemplary
physiological processes of a subject 110 (FIG. 1) having circadian
rhythms, which when lost or changed from a baseline, may be
associated with an occurrence of impending heart failure or other
disease state. In varying examples, one or more of the circadian
rhythms associated with the physiological processes 602-628 are
used to predict, monitor, or treat an occurrence of impending heart
failure in the subject 110. In certain examples, time detectors,
such as a time of the circadian rhythm representative signals
sensed or received, an arrhythmia incidence, or a clinical event,
are used additionally or alternatively to predict, monitor, or
treat the occurrence of impending heart failure. While the
following discusses exemplary physiological processes 602-628
having circadian rhythms whose presence, absence, or baseline
change is statistically associated with an occurrence of impending
heart failure, the list is not meant to be exhaustive, and may
include other processes 622 not herein discussed.
[0092] In one example, the subject's peripheral or core body
temperature 602 is used as a physiological process having a certain
circadian rhythm, which when lost or changed from a baseline, may
be associated with impending heart failure. In healthy subjects,
the human body temperature follows a definite circadian rhythm. For
instance, in the late afternoon, a healthy subject's body
temperature can be as much as 2.degree. F. higher than in the
morning. This circadian rhythm, however, may begin to become less
pronounced or otherwise change several hours to several days before
the onset of a disease state, such as heart failure. Monitoring the
circadian rhythm associated with body temperature in such instances
and comparing the results to one or more baseline prediction
criteria derived from one or more subjects in a non-disease state,
provides a tool to predict, monitor, or treat an occurrence of
impending heart failure. In one example, the circadian rhythm
associated with the subject's body temperature is measured by a
temperature sensor 260 (FIG. 2), such as a temperature capsule
embedded under the skin.
[0093] In another example, the subject's heart rate or heart rate
variability ("HRV") 604 is used as a physiological process having a
certain circadian rhythm, which when lost or changed from a
baseline, may be associated with impending heart failure. In
healthy subjects having HRV, the heart rate intervals have a
circadian rhythm, with HRV generally increasing during periods of
sleep. This circadian rhythm, however, may become less pronounced,
more irregular, or otherwise change several hours to several days
before the onset of a disease state, such as heart failure.
Monitoring HRV in such instances and comparing the variability to
one or more baseline prediction criteria derived from one or more
subjects in a non-disease state, provides a tool to predict,
monitor, or treat an occurrence of impending heart failure. In one
example, the circadian rhythm associated with HRV is determined by
standard deviation, variance, or other characteristic indicative of
variability. In another example, the circadian rhythm associated
with HRV is measured by a heart rate/heart rate variability sensor
266 (FIG. 2).
[0094] In a similar manner, the subject's heart rate may also be
used in the prediction of impending heart failure. In healthy
subjects, the heart rate follows a certain circadian rhythm. For
instance, a healthy subject's heart rate is typically lower during
the sleep hours than during the awake hours. This circadian rhythm,
however, may become lost or change from a baseline several hours to
several days before the onset of a disease state, such as heart
failure. In many instances, heart rate 604 during sleep may
actually increase before the onset of the disease state and lower
frequency components of HRV 604 associated with abnormal
sympathetic activation may also increase.
[0095] In another example, the subject's blood pressure 606 is used
as a physiological process having a certain circadian rhythm, which
when lost or changed from a baseline, may be associated with
impending heart failure. In healthy subjects, blood pressure
follows a circadian rhythm. For instance, the blood pressure
typically rises in the morning and stays elevated until late
afternoon, at which time it drops off and hits its lowest point
during the night. This circadian rhythm, however, may begin to
become less pronounced or otherwise change several hours to several
days before the onset of a disease state, such as heart failure.
Monitoring the circadian rhythm associated with blood pressure in
such instances and comparing the results to one or more baseline
prediction criteria derived from one or more subjects in a
non-disease state, provides a tool to predict, monitor, or treat an
occurrence of impending heart failure. In one example, the
circadian rhythm associated with the subject's blood pressure is
measured by a blood pressure sensor 258 (FIG. 2).
[0096] In another example, the subject's respiratory rate or
respiratory rate variability ("RRV") 608 is used as a physiological
process having a certain circadian rhythm, which when lost or
changed from a baseline, may be associated with impending heart
failure. In healthy subjects, the respiratory rate variability
follows a circadian rhythm. This circadian rhythm, however, may
become lost or change from a baseline several hours to several days
before the onset of a disease state, such as heart failure.
Indications of a loss or baseline change of circadian rhythm may
include a low frequency component of the subject's respiratory rate
decreasing (as the subject is less likely to be active), and a high
frequency component increasing. Monitoring respiratory rate in such
instances and comparing the variability to one or more baseline
prediction criteria derived from one or more subjects in a
non-disease state, provides a tool to predict, monitor, or treat an
occurrence of impending heart failure. In one example, the
circadian rhythm associated with RRV is measured by a respiratory
rate sensor 262 (FIG. 2). In one such example, the respiratory rate
sensor 262 includes an implantable breathing rate module which
includes a fiducial point detector adapted to detect a fiducial
point on the breathing signal that occurs a known number of one or
more times during the breathing cycle and a timer measuring the
time interval between respective successive fiducial points. In
another such example, the respiratory rate sensor 262 includes an
implantable transthoracic impedance sensor to peak-detect,
level-detect, or otherwise detect impedance variations resulting
from breathing, such as is described in Dalal et al., U.S. patent
application Ser. No. 11/458,602 entitled, "SLEEP STATE DETECTION,"
which is assigned to Cardiac Pacemakers, Inc.
[0097] In another example, the subject's tidal volume or minute
ventilation ("MV") 610 is used as a physiological process having a
certain circadian rhythm, which when lost or changed from a
baseline, may be associated with impending heart failure. In
healthy subjects, tidal volume and MV follow a circadian rhythm.
For instance, when plotted on a number of events vs. MV counts
histogram graph, an upper portion of a MV histogram represents
daytime MV, while a lower portion represents nighttime MV. This
circadian rhythm, however, may begin to become less pronounced or
otherwise change several hours to several days before the onset of
a disease state, such as heart failure. Monitoring the circadian
rhythm associated with tidal volume or minute ventilation in such
instances and comparing the results to one or more baseline
prediction criteria derived from one or more subjects in a
non-disease state, provides a tool to predict, monitor, or treat an
occurrence of impending heart failure. In one example, the
circadian rhythm associated with the subject's tidal volume or
minute ventilation is measured by an internal sensor 262 (FIG. 2),
such as a rate detector and an impedance sensor.
[0098] In another example, the subject's activity level 612 is used
as a physiological process having a certain circadian rhythm, which
when lost or changed from a baseline, may be associated with
impending heart failure. In healthy subjects, activity level
follows a circadian rhythm. This circadian rhythm, however, may
begin to become less pronounced or otherwise change several hours
to several days before the onset of a disease state, such as heart
failure. Indications of a loss or baseline change of circadian
rhythm may include a decrease in the subject's activity level.
Monitoring the circadian rhythm associated with activity level in
such instances and comparing the results to one or more baseline
prediction criteria derived from one or more subjects in a
non-disease state, provides a tool to predict, monitor, or treat an
occurrence of impending heart failure. In one example, the
circadian rhythm associated with the subject's activity level is
measured by an activity level sensor 270 (FIG. 2). In another
example, the circadian rhythm associated with the subject's
activity level is measured using, at least in part, an indication
of activity level input into a nearby external user interface 120
(FIG. 2) by the subject.
[0099] In another example, the subject's posture 614 is used as a
physiological process having a certain circadian rhythm, which when
lost or changed from a baseline, may be associated with impending
heart failure. In healthy subjects, posture follows a circadian
rhythm. This circadian rhythm, however, may begin to become less
pronounced, more irregular, or otherwise change several hours to
several days before the onset of a disease state, such as heart
failure. Indications of a loss or baseline change of circadian
rhythm may include the subject's increasingly supine posture
orientation. Monitoring the circadian rhythm associated with
posture in such instances and comparing the results to one or more
baseline prediction criteria derived from one or more subjects in a
non-disease state, provides a tool to predict, monitor, or treat an
occurrence of impending heart failure. In one example, the
circadian rhythm associated with the subject's posture is measured
by a posture sensor 268 (FIG. 2), such as a two-axis accelerometer
having Model No. ADXL202E, manufactured by Analog Device, Inc. of
Norwood, Mass., U.S.A. In another example, the subject's posture is
measured using techniques described in Hatlestad et al., U.S. Pat.
No. 7,226,422, entitled "DETECTION OF CONGESTION FROM MONITORING
PATIENT RESPONSE TO RECUMBENT POSITION," which is also assigned to
Cardiac Pacemakers, Inc.
[0100] In another example, the pattern of the subject's wake/sleep
cycle 618 is used as a physiological process having a certain
circadian rhythm, which when lost or changed from a baseline, may
be associated with impending heart failure. In healthy subjects,
sleep patterns follow an organized circadian rhythm. For instance,
one is most likely to sleep soundly when his/her temperature is
lowest, in the early morning hours, and most likely to awaken when
his/her temperature starts to rise around 6:00-8:00 am. This
circadian rhythm, however, may begin to become less organized
several hours to several days before the onset of a disease state,
such as heart failure. Monitoring the circadian rhythm associated
with sleep patterns 618 in such instances and comparing the results
to one or more baseline prediction criteria derived from one or
more subjects in a non-disease state, provides a tool to predict,
monitor, or treat an occurrence of impending heart failure.
[0101] The circadian rhythm associated with the subject's
wake/sleep cycle 618 may be measured by an internal sleep detector
272 (FIG. 2), which in some examples determines both the amount of
quality of the subject's sleep. One example of a sleep detector is
described in Carlson et al., U.S. Pat. No. 6,678,547 entitled,
"CARDIAC RHYTHM MANAGEMENT SYSTEM USING TIME-DOMAIN HEART RATE
VARIABILITY INDICIA," which is assigned to Cardiac Pacemakers, Inc.
Another example of a sleep detector is described in Dalal et al.,
U.S. patent application Ser. No. 11/458,602 entitled, "SLEEP STATE
DETECTION," which is assigned to Cardiac Pacemakers, Inc. Yet
another example of a sleep detector is described in Ni et al., U.S.
patent application Ser. No. 10/309,771 entitled, "SLEEP DETECTION
USING AN ADJUSTABLE THRESHOLD," which is assigned to Cardiac
Pacemakers, Inc. Alternatively, the subject 110 or caregiver may
enter an indication of his/her sleep quality or duration into an
external user interface 120 or 122 (FIG. 2).
[0102] In another example, the subject's thoracic impedance 624 is
used as a physiological process having a certain circadian rhythm,
which when lost or changed from a baseline, may be associated with
impending heart failure. In healthy subjects, thoracic impedance
624 follows a circadian rhythm in which impedance is lower during
the night and early morning hours and higher during the mid-to-date
afternoon. This circadian rhythm, however, may begin to shift,
become less pronounced, or otherwise change several hours to
several days before the onset of a disease state, such as heart
failure. Monitoring the circadian rhythm associated with thoracic
impedance 624 and comparing the results to one or more baseline
prediction criteria derived from one or more subjects in a
non-disease state, provides a tool to predict, monitor, or treat an
occurrence of impending heart failure. In one example, the
circadian rhythm associated with the subject's thoracic impedance
624 is measured by injecting an electrical stimulus current of
known or attainable value (e.g., via the ventricular 256 or atrial
252 stimulation circuits) to one or more implanted electrodes and
measuring (e.g., via the ventricular 254 or atrial 250 sense
circuits) the resulting voltage using one or more other implanted
electrodes. Using information about the current and the resulting
voltage, the IMD 102 may calculate an impedance by taking a ratio
of resulting voltage to injected current.
[0103] In yet another example, the subject's heart sounds 628 (for
example, heart sounds referred to in the art as S.sub.1, S.sub.2,
and particularly the heart sound referred to in the art as S.sub.3)
are used as a physiological process having a certain circadian
rhythm, which when lost or changed from a baseline, may be
associated with impending heart failure. In healthy subjects, heart
sounds 628 follow a circadian rhythm. This circadian rhythm,
however, may begin to become less pronounced, change frequency, or
otherwise change several hours to several days before the onset of
a disease state, such as heart failure. In one example, the
circadian rhythm associated with the subject's heart sounds 628 is
measured by an implantable accelerometer, microphone or other
implantable sensor, such as by using the systems and methods
described by Lincoln et al., U.S. Pat. No. 6,665,564 entitled,
"CARDIAC RHYTHM MANAGEMENT SYSTEM SELECTING A-V DELAY BASED ON
INTERVAL BETWEEN ATRIAL DEPOLARIZATION AND MITRAL VALVE CLOSURE,"
or the systems and methods described in Lincoln et al., U.S. Pat.
No. 6,963,777 entitled, "CARDIAC RHYTHM MANAGEMENT SYSTEM AND
METHOD USING TIME BETWEEN MITRAL VALVE CLOSURE AND AORTIC
EJECTION," each of which is assigned to Cardiac Pacemakers, Inc. In
another example, the heart sounds 628 are measured by a caregiver
while the subject is lying on his/her side, and a numerical value
indicative of a heart sound frequency of amplitude is input into an
external user interface 120, 122 (FIG. 2), by the caregiver.
[0104] Alternatively or additionally, a time of the circadian
rhythm representative signals sensed or received, an arrhythmia
incidence, or a clinical event may be used to predict, monitor, or
treat an occurrence of impending disease. As one example, the time
of a subject's clinical event is entered into an external
user-interface 120, 122 and used to predict, monitor, or treat the
occurrence of impending disease. Admissions to the emergency room
for pulmonary edema not associated with acute myocardial infarction
is highest between 8:00 am-Noon and 8:00 pm-12:00 am and lowest
between Noon-8:00 pm. Thus, clinical admission in combination with
a reduced body temperature 602 in the late afternoon, for instance,
may indicate the onset of a disease state, such as heart
failure.
[0105] As another example, the time of a subject's arrhythmia or
abnormal breathing incidence (e.g., apnea, hypopnoea, or periodic
breathing) is used to predict, monitor, or treat the occurrence of
impending disease. A cardiac arrhythmia incidence is any disorder
of the heart rate or rhythm. The presence of one or more cardiac
arrhythmias may correlate to an occurrence of impending heart
failure. In one example, as discussed above, the IMD 102 (FIG. 2)
may utilize an atrial 252 (FIG. 2) and ventricular 254 (FIG. 2)
sensing circuit to sense cardiac signals for determining whether a
rhythm is normal or pathologic. In another example, the subject or
caregiver enters a detected presence of one or more cardiac
arrhythmia, found using an echocardiogram or other imaging
instrument, into an external user interface 120 or 122 (FIG.
2).
[0106] FIGS. 7A-7C illustrate exemplary graphs that may be
generated by system 100 and which illustrate circadian rhythm
comparisons that may be made between circadian rhythms associated
with at least one physiological process sensed or received and one
or more baseline circadian rhythm prediction criteria. These
illustrations, when displayed on an external user interface 120,
122 display screen (FIG. 1), for example, may be used by a subject
110 (FIG. 1) or caregiver to predict, monitor, or treat an
occurrence of impending heart failure or other disease state.
[0107] FIG. 7A illustrates circadian rhythms associated with
respiration rate 608 (FIG. 6) plotted on a respiration rate
(breaths/minute) vs. time (hours) graph. As shown, the respiration
circadian rhythm of a healthy subject 700 includes a pronounced,
regular pattern; whereas, the respiration circadian rhythm of an
unhealthy subject 702 includes a less pronounced and irregular
pattern. More specifically, the unhealthy subject has a higher
maximum respiratory rate, a higher mean/median respiratory rate,
and less variability in minimum/mean/median respiratory rate in
comparison to the healthy subject. Since the respiration circadian
rhythm of the unhealthy subject 702 is lost or changed relative to
the healthy subject's baseline circadian rhythm 700, a prediction
of impending disease, such as heart failure, may have been in order
for the unhealthy subject as soon as such loss or change can be
made with a reasonable degree of certainty.
[0108] FIG. 7B illustrates an alternative way to graphically
illustrate circadian rhythms associated with respiration rate 608
(FIG. 6) of a healthy and unhealthy subject. In FIG. 7B,
conceptualized (i.e., not real) data of the daily variability of
the respiratory rate is plotted against the daily median of the
respiratory rate. In this conceptualization, the healthy subject
700 maintains a lower median respiratory rate and higher
variability in the mean respiratory rate than the unhealthy subject
702. Among other things, such characteristics of the healthy
subject may indicate an easier time breathing and a greater
activity level than the unhealthy subject.
[0109] FIG. 7C illustrates a circadian rhythm associated with a
subject's wake/sleep cycle 618 (FIG. 6). Initially, on days 1-3,
the subject follows a substantially regular sleep schedule,
including sleeping from about 12:00-5:00 am each day. Such regular
sleep schedule is indicative of a healthy subject 700. In contrast,
on days 4-8, the subject follows a very irregular sleep schedule.
For instance, on day 4, the subject sleeps from about 12:00-1:00 am
and 4:30-5:30 am. Then, on day 5, the subject sleeps from about
9:00 pm-12 00 am, 1:00-4:30 am, and from 5:30-6:00 am. Such
irregular sleep schedule is indicative of a unhealthy subject
702.
[0110] Subjects with severe heart failure often suffer from
inability to sleep either due to pulmonary congestion or inability
to tolerate a supine posture. In addition, evidence from sleep
studies indicate that as a person nears death, the time in which
the subject sleeps becomes much more fragmented. By visually seeing
the sleep regularity (or irregularity, as it may be), caregivers
(or the subject themselves) may be able to determine if the
subject's health state is changing due to a possible worsening in
disease state. Since the sleep circadian rhythm of the unhealthy
subject 702 is lost or changed relative to the healthy subject's
baseline circadian rhythm 700, a prediction of impending disease,
such as heart failure, may have been in order for the unhealthy
subject as soon as such loss (marked by irregularity) could be made
with a reasonable degree of certainty.
[0111] FIG. 8 illustrates one example of a method 800 of
predicting, monitoring, or treating an occurrence of impending
disease, such as heart failure, in a subject. At 802, one or more
baseline circadian rhythm prediction criteria are stored. This may
be accomplished in a number of ways. In one example, the one or
more baseline circadian rhythm prediction criteria are loaded into
an IMD before, during, or after the IMD is implanted in the
subject. The one or more baseline circadian rhythm prediction
criteria may be established in a number of ways. In one example,
the one or more baseline circadian rhythm prediction criteria are
derived using one or more past physiological process observation of
the subject when in a non-disease health state. In another example,
the one or more baseline circadian rhythm prediction criteria are
derived using one or more past physiological process observation of
a population in a non-disease health state.
[0112] At 804, at least one physiological process having a
circadian rhythm whose presence, absence, or baseline change is
statistically associated with a disease state, is sensed or
received. This may be accomplished in a number of ways. In one
example, the at least one physiological process having the
circadian rhythm is sensed or received via a physiological
information collection device. The circadian rhythm representative
signals sensed or received may be associated with various
physiological processes, such as body temperature (core or
peripheral), heart rate, heart rate variability, respiration rate,
respiration rate variability, minute ventilation, activity, blood
pressure, posture, tidal volume, sleep quality or duration,
thoracic impedance, or heart sounds.
[0113] At 806, the circadian rhythm associated with the at least
one physiological process sensed or received is compared with the
one or more baseline circadian rhythm prediction criteria. This may
be accomplished in a number of ways. In one example, a probability
comparator of an impending disease state prediction module compares
one or more sensed or received circadian rhythm representative
signal (S.sub.1, S.sub.2, . . . , S.sub.N) values to corresponding
baseline circadian rhythm prediction criteria (C.sub.1, C.sub.2, .
. . , C.sub.N) values. When the values of the circadian rhythm
representative signals sensed or received differ by more than a
specified amount from the baseline circadian rhythm prediction
criteria, thereby indicating a loss or baseline change of circadian
rhythm, a positive probability indication of the occurrence of
impending heart failure results at 808. When the values of the
circadian rhythm representative signals sensed or received are
substantially similar to the baseline circadian rhythm prediction
criteria, therefore indicating no substantial loss or baseline
change of circadian rhythm, a negative probability indication of
the occurrence of impending heart failure results at 810 and the
process returns to 804.
[0114] Optionally, at 812, each circadian rhythm representative
signal (S.sub.1, S.sub.2, . . . , S.sub.N) value differing from the
corresponding baseline circadian rhythm prediction criteria
(C.sub.1, C.sub.2, . . . , C.sub.N) value by more than the
specified amount is weighted. This may be accomplished in a number
of ways. In one example, for each circadian rhythm representative
signal (S.sub.1, S.sub.2, . . . , S.sub.N) value differing from the
corresponding baseline circadian rhythm prediction criteria
(C.sub.1, C.sub.2, . . . , C.sub.N) value by more than the
specified amount, a weighting module of the impending disease state
prediction module stores weighting factors (Weight.sub.1,
Weight.sub.2, . . . , Weight.sub.N). In another example, each
weighting factor (Weight.sub.1, Weight.sub.2, . . . , Weight.sub.N)
provides a degree to which each circadian rhythm representative
signal differing from the corresponding baseline circadian rhythm
prediction criteria by more than the specified amount enters into a
probability indication computed at 814. In yet another example,
each weight is computed using not only information about which
physiological process the circadian rhythm relates to, but also
using information about which other physiological processes having
circadian rhythms are also being used to predict the occurrence of
impending heart failure.
[0115] At 816, a probability assertion or statement of impending
heart failure is made. This may be accomplished in a number of
ways. In one example, a prediction processing module of the
impending disease state prediction module generates, using the
probability indication output, a probability assertion or statement
that a heart failure will occur (e.g., within a specified time
period after the prediction). In another example, at least one of
the sensing or receiving, comparing, or predicting is performed, at
least in part, implantably.
[0116] At 818, an alert of the predicted occurrence of impending
heart failure decompensation is provided to the subject or a
caregiver. The alert may be communicated in a number of ways. In
one example, an audible tone is sounded. In another example, the
subject is linked up to a remote monitoring system (e.g., via a
communication repeater) thereby allowing the alert to be
electronically communicated to the caregiver for review. In yet
another example, muscle tissue in the locality of the IMD within
the subject is stimulated. In a further example, the alert includes
transmitting information about the predicted occurrence of
impending heart failure and information used to make the prediction
to an external user interface. In this way, the information used to
make the prediction may be presented to the subject or caregiver on
the interface's LCD or other display.
[0117] At 820, one or more appropriate therapies are selected
(e.g., drug therapy or neurostimulation). In one example, one or
more heart failure preventive therapy is selected. In another
example, one or more therapy secondarily related to heart failure
is selected. The therapy selection may be accomplished in a number
of ways. In one example, a therapy selection module selects the one
or more appropriate preventive or other therapies. At 822, a
therapy is initiated using the predicted occurrence of impending
heart failure (e.g., within a specified prediction time period).
This may be accomplished in a number of ways. In one example, an
control module activates the selected therapy via an output to a an
atrial stimulation circuit, a ventricular stimulation circuit, a
neural stimulation circuit, or a drug pump.
[0118] By monitoring post-therapy circadian rhythms, the efficacy
and necessary amount of therapy may be determined. To this end, at
least one physiological process having a circadian rhythm, whose
presence, absence, or baseline change is statistically associated
with a disease state, is sensed or received at 824. This may be
accomplished in a number of ways, such as those discussed in regard
to the method at 804. At 826, the circadian rhythm associated with
the at least one physiological process sensed or received is
compared with the one or more baseline circadian rhythm prediction
criteria, such as at 806. When the values of the circadian rhythm
representative signals sensed or received differ by more than a
specified amount from the baseline circadian rhythm prediction
criteria, an increase in the amount of selected therapy may be
warranted at 828. When the values of the circadian rhythm
representative signals sensed or received are substantially similar
to the baseline circadian rhythm prediction criteria, a titration
of the selected therapy may be warranted at 828; additionally, if
applicable, a discharge of the subject from the hospital may be
reasonable at 830.
CONCLUSION
[0119] Heart failure is a common clinical entity, particularly
among the elderly, but is often not treated (if at all) until the
disease is detected late in the disease process via associated
physical symptoms, such as abnormal thoracic fluid build-up behind
the heart. Advantageously, the present systems and methods allow
for the prediction, monitoring, or treatment of impending heart
failure or other disease states by monitoring one or more circadian
rhythms associated with a subject's physiological process.
Practically every physiological process in the human body exhibits
circadian rhythmicity, and thus, the monitoring of circadian rhythm
provides an adequate means for predicting, monitoring, or treating
an impending disease state, such as heart failure or heart failure
decompensation, among others.
[0120] The time savings provided by prediction (as opposed to
detection alone), may reduce or eliminate expensive hospitalization
and aid in avoiding a decompensation crisis or properly managing a
heart failure subject, for example, in a state of relative
well-being. Further, the present systems and methods provide an
alert to the subject or the subject's caregiver regarding a
positive prediction of impending heart failure or other disease
state. Further yet, the present systems and methods may adjust (or
initiate) a therapy (e.g., drug therapy or neuro stimulation) to
prevent or minimize impending disease state using the prediction
and monitor the efficacy of such therapy (including monitoring the
subject's recovery).
[0121] While the present systems and methods may be used to monitor
process rhythms on a variety of cycle periods, such as circadian,
circaseptan, circatrigintan, circannual, holidays, weekdays,
weekends, or menstrual, collectively "chronobiological rhythms", a
majority of the foregoing description is cast in terms of circadian
rhythm monitoring for exemplary purposes. Such description is not
intended, however, to limit the scope of the present subject matter
in any way. Furthermore, a loss or baseline change of
chronobiological (e.g., circadian) rhythm may signify an occurrence
of an impending disease state other than just heart failure. For
instance, a breakdown in chronobiological rhythm may occur during
general sickness (e.g., a flu or cold), neurological, mental or
pulmonary disease, a viral or bacterial infection, other
cardiovascular diseases (e.g., diabetes) or even cancer. As such,
this patent document is intended to be commensurate in scope to
cover these additional embodiments.
[0122] It is to be understood that the above description is
intended to be illustrative, and not restrictive. For example, the
above-described embodiments (or aspects thereof) may be used in
combination with each other. Many other embodiments will be
apparent to those of skill in the art upon reviewing the above
description. The scope of the present systems and methods should
therefore, be determined with reference to the appended claims,
along with the full scope of legal equivalents to which such claims
are entitled. In the appended claims, the term "including" is used
as the plain-English equivalents of the respective terms
"comprising" and "wherein." Also, in the following claims, the
terms "including" and "comprising" are open-ended, that is, a
system, device, article, or process that includes elements in
addition to those listed after such a term in a claim are still
deemed to fall within the scope of that claim. Moreover, in the
following claims, the terms "first," "second," and "third," etc.
are used merely as labels, and are not intended to impose numerical
requirements on their objects.
[0123] The Abstract of the Disclosure is provided to comply with 37
C.F.R. .sctn.1.72(b), requiring an abstract that will allow the
reader to quickly ascertain the nature of the technical disclosure.
It is submitted with the understanding that it will not be used to
interpret or limit the scope or meaning of the claims. In addition,
in the foregoing Detailed Description, various features may be
grouped together to streamline the disclosure. This method of
disclosure is not to be interpreted as reflecting an intention that
the claimed embodiments require more features than are expressly
recited in each claim. Rather, as the following claims reflect,
inventive subject matter may lie in less than all features of a
single disclosed embodiment. Thus the following claims are hereby
incorporated into the Detailed Description, with each claim
standing on its own as a separate embodiment.
* * * * *